自从深网的成立以来，训练模型所需的计算资源一直在增加。大规模数据集中的培训神经网络已成为一项具有挑战性且耗时的任务。因此，需要减少数据集而不损害准确性。在本文中，我们介绍了一种早期方法，即通过均匀聚类来减少数据集大小的新颖方法。所提出的方法基于将数据集划分为均匀簇的想法，并选择对准确性产生显着贡献的图像。我们提出了两种变体：用于图像数据降低的几何均匀聚类（GHCIDR）和合并GHCIDR在基线算法 - 通过均匀聚类（RHC）降低（RHC），以实现更好的准确性和训练时间。 GHCIDR背后的直觉涉及通过簇权重和训练集的几何分布选择数据点。合并GHCIDR涉及使用完整的链接聚类的群集合并相同的标签。我们使用了三个深度学习模型 - 完全连接的网络（FCN），VGG1和VGG16。我们在四个数据集中进行了两个变体 - MNIST，CIFAR10，Fashion-Mnist和Tiny-Imagenet。与RHC相同百分比的合并GHCIDR在MNIST，Fashion-Mnist，CIFAR10和Tiny-Imagenet上分别增加了2.8％，8.9％，7.6％和3.5％。

在本文中，我们介绍了一种早期方法的新颖变化，称为均质聚类算法，用于降低数据集大小。本文提出的方法背后的直觉是将数据集划分为均匀簇，并选择一些对准确性产生重大贡献的图像。选定的图像是训练数据的正确子集，因此是可读的。我们在基线算法RHC上提出了四个变体。第一种方法背后的直觉是，边界点有助于簇的代表。它涉及选择群集质心的最远的k和一个最近的邻居。在以下两种方法（KONCW和CWKC）中，我们介绍了簇权重的概念。它们是基于这样一个事实，即较大的簇贡献比较小的群集的贡献更多。最终变化是GHCIDR，它根据数据分布的几何方面选择点。我们在两个深度学习模型 - 完全连接的网络（FCN）和VGG1上进行了实验。我们在三个数据集中的四个变体中进行了实验：MNIST，CIFAR10和Fashion-Mnist。我们发现，GHCIDR的最佳准确度分别为99.35％，81.10％和91.66％，培训数据降低了87.27％，32.34％和76.80％，分别为MNIST，CIFAR10和时尚。

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.

We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e.g. a robotic manipulator in a simulated kitchen. Instead of imitating low-level actions like joint velocities, our approach imitates the sequence of demonstrated semantic skills like "opening the microwave" or "turning on the stove". This allows us to transfer demonstrations across environments (e.g. real-world to simulated kitchen) and agent embodiments (e.g. bimanual human demonstration to robotic arm). We evaluate on three challenging cross-domain learning problems and match the performance of demonstration-accelerated RL approaches that require in-domain demonstrations. In a simulated kitchen environment, our approach learns long-horizon robot manipulation tasks, using less than 3 minutes of human video demonstrations from a real-world kitchen. This enables scaling robot learning via the reuse of demonstrations, e.g. collected as human videos, for learning in any number of target domains.

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments like our homes, schools, and hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, while modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. But learned visual navigation policies have predominantly been evaluated in simulation. How well do different classes of methods work on a robot? We present a large-scale empirical study of semantic visual navigation methods comparing representative methods from classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: modularity and abstraction in policy design enable Sim-to-Real transfer. For researchers, we identify two key issues that prevent today's simulators from being reliable evaluation benchmarks - (A) a large Sim-to-Real gap in images and (B) a disconnect between simulation and real-world error modes - and propose concrete steps forward.

We consider the problem of embodied visual navigation given an image-goal (ImageNav) where an agent is initialized in an unfamiliar environment and tasked with navigating to a location 'described' by an image. Unlike related navigation tasks, ImageNav does not have a standardized task definition which makes comparison across methods difficult. Further, existing formulations have two problematic properties; (1) image-goals are sampled from random locations which can lead to ambiguity (e.g., looking at walls), and (2) image-goals match the camera specification and embodiment of the agent; this rigidity is limiting when considering user-driven downstream applications. We present the Instance-specific ImageNav task (InstanceImageNav) to address these limitations. Specifically, the goal image is 'focused' on some particular object instance in the scene and is taken with camera parameters independent of the agent. We instantiate InstanceImageNav in the Habitat Simulator using scenes from the Habitat-Matterport3D dataset (HM3D) and release a standardized benchmark to measure community progress.

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSEM from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSEM perform outperform those trained on prior datasets. Introduction of HM3DSEM in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022.

我们提出了一种将运动传递给静止2D图像的方法。我们的方法使用深度学习将图像的一部分划分为主题，然后使用绘制来完成背景，并最终通过将图像嵌入三角形网格中，同时保留其余图像，从而在主题中添加动画。

由于其在崎rough的地形中的高机动性和遍历性，四倍的平台已成为一个积极的研究主题。但是，确定机器人是否可以通过裂缝环境以及如何准确计算其路径是高度挑战。此外，计算出的路径可能会穿过具有动态物体或环境对机器人或周围人危险的区域。因此，我们提出了一种新颖的概念方法，即通过虚拟现实（VR）中的用户指导路径计划进行教学四倍的机器人导航。我们的系统包含全球和本地路径计划者，使机器人可以通过学习的迭代来生成路径。 VR接口允许用户与环境进行交互，并在具有挑战性的情况下协助四足机器人。比较实验的结果表明，人与路径计划算法之间的合作可以使算法的计算速度平均增加35.58％，并且在测试方案中，路径长度（平均6.66％）的非急剧增加。此外，用户将VR接口描述为不需要物理需求（10中的2.3），并高度评估了其性能（10中的7.1分）。寻找不太最佳但更安全的路径的能力仍然需要在混乱和非结构化的环境中导航的任务。